---
title: "Single-Touch Edge AI Platform"
outcome: "Turned a high-level edge-AI design into a single-press deployment running on Kubernetes at the store edge."
summary: "Store-edge Kubernetes running GPU-backed AI workloads, deployed from one command, with readiness-gated GPUs so inference never starts before the hardware is ready."
role: "Infrastructure / DevOps Engineer · Woolworths"
period: "2025 – Present"
stack: ["Kubernetes", "Edge", "NVIDIA GPU", "CD pipelines", "Helm", "Python"]
featured: true
order: 10
cover: "edge-ai-platform"
diagram: "edge-ai"
---

## Problem

Edge AI at retail scale lives or dies on repeatability. A computer-vision workload that
runs perfectly in a lab has to come up the same way in a store with no on-site engineer,
flaky connectivity, and a GPU that may not be ready the instant Kubernetes wants to schedule
against it. The starting point was a high-level design and a pile of manual steps — exactly
the gap between "it works" and "it ships."

## Constraints

- **No hands at the edge.** Deployment has to be hands-off and idempotent — a single press.
- **GPU timing.** Inference pods must never schedule before the GPU device plugin is healthy,
  or they crash-loop and poison the rollout.
- **Heterogeneous stores.** Per-site variables (network, hardware, identity) without forking
  the platform for every location.

## Design

I took the high-level designs and turned them into low-level, problem-solving deployments
driven by CD pipelines. The application is packaged as containers and shipped to a
store-edge Kubernetes cluster via Helm with end-state manifests. Per-store configuration is
injected from a single source of truth, so one pipeline produces a correct deployment for
any site.

The load-bearing piece is **readiness gating**: Bash/Shell probes and Kubernetes watchdogs
confirm the GPU device plugin is up *before* inference pods are allowed to run, and pod
lifecycle management keeps the workload honest from there.

## Security & reliability decisions

- **Init-gated GPU readiness** — the single biggest reliability win; no more pods racing the
  GPU at boot.
- **Single source of truth** for config — drift can't creep in store-to-store.
- **Spec-driven, documented-as-code** — the deployment *is* the documentation.

## Outcome

A high-level idea becomes a real, repeatable deployment on a single press. New edge sites
come up consistently, GPUs come online reliably, and the manual runbook is gone — replaced
by a pipeline anyone on the team can trigger.

## Future improvements

Push more of the per-store delta into declarative policy, and extend the readiness model to
cover the full inference dependency chain (model artifacts, egress, downstream sinks) as a
single health gate.
